Study of Long-Term Pavement Performance (LTPP): Pavement Deflections

Chapter 1. Introduction

BACKGROUND

The Long-Term Pavement Performance (LTPP) program, which began
as part of the Strategic Highway Research Program (SHRP) and is now
administered by the Federal Highway Administration (FHWA), has been
gathering falling weight deflectometer (FWD) load-deflection data
since late 1988. The FWD database is large—by the fall of 1998,
there were already more than four million records (or lines) of
load-deflection data, representing FWD tests conducted throughout
the United States, Canada, and Puerto Rico. In addition, a
considerable volume of ancillary information—such as sensor
calibrations, pavement temperatures, sensor positions, and FWD
operator observations and comments—exists as well.

The FWD database was used for comparison to screen the
pre-autumn 1998 FWD level E data for errors or anomalies. The term
level E refers to those data elements that have undergone a
screening process already, and have been uploaded to the LTPP
database for public dissemination and use. One common source of
these data, and other LTPP data elements as well, is data from the
various versions of DataPave.

STUDY OBJECTIVES

FWD load-deflection data are generally used to characterize the
tested pavement by an analysis of the applied load and the
magnitudes (or shape) of the measured deflection basin. Often,
these data are used to backcalculate layered elastic stiffnesses or
moduli. The results give the pavement researcher a measure of the
pavement’s bearing capacity, which can in turn be linked to future
pavement performance.

The primary objective of this study was to identify data errors
or anomalies in the LTPP loaddeflection database that were not
identified during routine data screening required to reach level E.
Routine screening applies more general procedures, such as broad
range checks, to the data. The intent of this study was to review
the level E deflection data and ancillary information, looking for
data discrepancies and errors that routine screening may not have
identified. The overall objective of the postscreening, final data
check was to assure that good quality load-deflection and ancillary
data are available for researchers and highway engineers.

The majority of the errors and anomalies found during this
quality assurance (QA) screening of the level E FWD database either
have been, or are in the process of being, corrected. The resulting
database will, in turn, be much more useful for pavement analysis
and design engineers who wish to understand and properly evaluate
new or rehabilitated highway pavements, based on the FWD
load-deflection data in the level E database, generally using an
up-to-date version of DataPave. This report documents the screening
methodologies employed and the findings of this study, along with
the extent of the various categories of errors and anomalies
identified and reported. Specific examples of these FWD-associated
data categories are also presented in this report.

SCOPE AND ORGANIZATION OF REPORT

It was initially expected that the primary thrust of this study
would be a straightforward screening for anomalies in the level E
load-deflection data, possibly accompanied by parallel anomalies in
the peak load readings. However, it was found that the majority of
the questionable FWD2 associated data in the database in fact did
not involve the deflection or load readings directly, but rather a
variety of other manual data entry errors or oversights. The number
and magnitude of direct equipment errors found were in fact
surprisingly small, with the vast majority of the data (well over
99.5 percent) appearing to be of very good quality—highly accurate
and very repeatable.

The following list is a breakdown of the various categories of
errors and anomalies found, together with the approximate
percentage of data affected by the questionable load-deflection
data in the pre-autumn 1998 database:

Inconsistent deflection basin anomalies: ~0.1 percent.

Systematic load-deflection anomalies: <0.1 percent.

Load-deflection calibration anomalies: None.

Long-term sensor positioning errors: ~7 percent.

Single section sensor positioning errors: 0.4 percent.

Lane designation errors: <0.1 percent.

Date- or time-stamp errors: 0.1 percent.

Drop height designation errors: 0.3 percent.

Site errors (tested at wrong test section): 0.1 percent.

Stationing errors: 0.1 percent.

As can be seen in the list of errors and anomalies, only the
first three categories are directly related to the FWD load and
deflection values present in the level E database. The remaining
categories have little or nothing to do with the quality of the
deflection data gathered; these anomalies are generally due to
inadvertent data entry errors, where manual keyboard input to the
field data collection program(s) is required. Moreover, the
approximate percentage of the pre-autumn 1998 data directly
affected by anomalous load-deflection readings is probably less
than 0.2 percent (by any reasonable measure, a very small
percentage of the FWD data), while the corresponding percentage
affected by other types of data errors may be greater than 8
percent. In fact, of all the error types identified, one category
of error dominates all other errors combined: Incorrectly
placed sensors along the FWD’s raise-lower bar over extended
periods of time. Still, the quality of the FWD data in the
database has to be regarded as excellent overall. As previously
noted, most of the FWD data anomalies identified by this study can
be (or already have been) either corrected or flagged.

Two other categories of anomalies or potential errors were also
identified in the pre-autumn 1998 FWD load-deflection data, as
follows:

Unbound layer anomalies (all data): Not screened (0.8
percent).

Unchanged (noted only) data discrepancies: ~ 1 percent.

These two categories of data were not recommended for alteration
or flagging in the database for several reasons. For the category
“unbound layer anomalies,” there is considerable variation in the
data for most FWD test points (even drop-to-drop at the same drop
height). Thus it was not possible to find any automated and
reasonable criteria to sort out the “good” from the “bad” data.
With respect to the general category “unchanged data
discrepancies,” it was deemed adequate to merely note the nature
and extent of each discrepancy found; no defendable changes,
deletions, or flags in the database could be justified based on
available information (see also chapter 6). Since it is possible
that the FWD test results (or at least many of these) are correct
(or nearly so) in both categories, no changes or flags are
recommended in the level E dataset for these categories of data
anomalies. 3 The information presented in the following chapters is
organized as follows. Chapter 2 describes the data obtained from
the LTPP level E database, along with an overview of how these data
have been organized. Load-deflection errors and anomalies are
discussed in chapters 3 and 4. Categories of nondeflection
associated manual data entry errors are covered in chapter 5.
Chapter 6 deals with other data anomalies that have been noted but
not recommended for changes or flags in the database, due to lack
of definitive information. Chapter 7 presents suggested computed
parameters and new FWD test procedures. A summary and conclusions
are presented in chapter 8. Appendices A through M are found at the
end of this report.